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An Overview of Computer Vision

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An Overview of Computer Vision U.S. Department of Commerce National Bureau of Standards NBSIR 82-2582 AN OVERVIEW OF COMPUTER VISION September 1982 f— QC Prepared for iuu National Aeronautics and Space . Ibo Administration Headquarters 61-2562 Washington, D.C. 20546 National Bureau ot Standard! OCT 8 1982 ftsVac.c-'s loo - 2% NBSIR 82-2582 AN OVERVIEW OF COMPUTER VISION William B. Gevarter* U.S. DEPARTMENT OF COMMERCE National Bureau of Standards National Engineering Laboratory Center for Manufacturing Engineering Industrial Systems Division Metrology Building, Room A127 Washington, DC 20234 September 1982 Prepared for: National Aeronautics and Space Administration Headquarters Washington, DC 20546 U.S. DEPARTMENT OF COMMERCE, Malcolm Baldrige, Secretary NATIONAL BUREAU OF STANDARDS, Ernest Ambler, Director * Research Associate at the National Bureau of Standards Sponsored by NASA Headquarters * •' ' J s rou . Preface Computer Vision * Computer Vision — visual perception employing computers — shares with "Expert Systems" the role of being one of the most popular topics in Artificial Intelligence today. Commercial vision systems have already begun to be used in manufacturing and robotic systems for inspection and guidance tasks. Other systems at various stages of development, are beginning to be employed in military, cartograhic and image inter pretat ion applications. This report reviews the basic approaches to such systems, the techniques utilized, applications, the current existing systems, the state-of-the-art of the technology, issues and research requirements, who is doing it and who is funding it, and finally, future trends and expectations. The computer vision field is multifaceted, having many participants with diverse viewpoints, with many papers having been written. However, the field is still in the early stages of development — organizing principles have not yet crystalized, and the associated technology has not yet been rationalized. Thus, this report is not as smooth and even as would be desirable. Nevertheless, this overview should prove useful to engineering and research managers, potential users and others who will be affected by this field as it unfolds. * Th i s re port Isi n support of the more general NBS/NASA report, An Overview of Artificial Intelligence and Robotics i . Acknowledgments I wish to thank the many individuals and organizations who have contributed to this report by furnishing information and suggestions. I particularly would like to thank Drs. T. Binford of Stanford, M. Brady of M.I.T., M. Tenenbaum and H. Barrow of Fairchild and Gennery et al of JPL for furnishing source material that was essential to the development of this report. In addition, I would like to thank the staff of the NBS Industrial Systems Division, CDMR R. Ohlander of DARPA, Drs. A. Rosenfeld and M. Schneer of U. of MD., M. Fischler of SRI, E. Sacerdoti of M.I.C. and J. Wilder of Object Recognition Systems for reviewing this report and suggesting corrections and modifications. However, any remaining errors or omissions must remain the responsibility of the author. I would also like to thank Margie Johnson for typing and facilitating the publication of this NBS series of reports on Artificial Intelligence and Robotics. It is not the intent of the National Bureau of Standards to recommend or endorse any of the manuf ac turers or organizations named in this report, but simply to attempt to provide an overview of the computer vision field. However, in such a diverse and rapidly changing field, important activities and products may not have been mentioned. Lack of such mention does not in any way imply that they are not also worthwhile. The author would appreciate having any such omissions or oversights called to his attention so that they can be considered for future reports Table of Contents Preface i I. Introduction 1 II. Definition 5 III. Origins of Computer Vision 7 IV. Relation to Human Vision 11 V. Applications 16 VI. Basis for a General Purpose Image Understanding System 19 VII. Basic Paradigms for Computer Vision 22 A. Hierarchical -- Bottom-up Approach 22 B. Hierarchical Top-down Approach 24 C. Heterarchical Approach 26 D. Blackboard Approach 26 VIII. Levels of Representation 28 IX. Research in Model-Based Vision Systems 34 X. Industrial Vision Systems 38 A. General Characteristics 38 B. Examples of Efforts in Industrial Visual Inspection Systems 40 C. Examples of Efforts in Industrial Visual Recognition and Location Systems 42 D. Commercially Available Industrial Vision Systems 44 XI. Who is Doing It 46 A. Research Oriented 46 B. Commercial Vision Manufacturers 46 XII. Who is Funding It 49 iii XIII. Summary of the State-of-the-Ar t 52 A. Human Vision 52 B. Low and Intermediate Levels of Processing ... 52 C. Industrial Vision Systems 54 D. General Vision Systems 56 1 . Introduction 56 2. Difficulties 57 3. Techniques 59 4. Conclusions 60 E. Visual Tracking 62 F. Overview 62 XIV. Current Problems and Issues 64 A. General 64 B. Techniques 65 1 . Low Level Processing 65 2. Middle Level Processing 65 3. High Level Processing 66 C. Representation and Modeling 66 D. System Paradigms and Design 66 E. Knowledge Acquisition — Teaching and Programming 67 F. Sensing 67 G. Planning 67 XV. Research Needed 68 A. General 68 B. Techniques 68 1 . Low Level Processing 68 2. Middle Level Processing 68 iv 3. High Level Processing 69 C. Representaion and Modeling 69 D. System Paradigms and Design 69 E. Knowledge Acquisition - Teaching and Programming 70 F. Sensing ..... 70 G. Planning 70 XVI. Future Trends 71 A. Techniques 71 B. Hardware and Architecture 71 C. AI and General Vision Systems 72 D. Modeling and Programming 73 E. Knowledge Acquisition 73 F. Sensing 73 G. Industrial Vision Systems 7^ H. Future Applications 75 I. Conclusion 77 References 78 Appendices A. Low Level Features and Representations 86 A. Pixels 86 B. Texture 86 C. Regions 86 D. Edges and Lines 87 E. Corners 87 B. Extracting Edges and Areas 88 A. Extracting Edges 88 1. Linear Matched Filtering 88 v 2. Non-Linear Filtering 88 3. Local Thresholding 92 4 . Surface Fitting 92 5. Rotationally Insensitive Operators .... 92 6. Line Following 92 7. Global Methods 92 B. Edge Finding Variations 94 C. Linking Edge Elements and Thinning Resultant Lines 94 D. Remarks on Edge Finding 95 E. Extracting Regions 96 £. Segmentation and Interpretation 98 A. The Computer Vision Paradigm 98 B. An Early Bottom-up Systems 98 C. Problems with Bottom-up Systems 99 D. Interpretation-Guided Segmentation 99 E. Use of General World Knowledge to Guide Segmentation 100 E). 2-D Representation, Description and Recognition 103 A. Pyramids 103 B. Quadtrees 103 C. Statistical Features of a Region 103 D. Boundary Curves 104 E. Run-Length Encoding 104 F. Skeleton Representations and Generalized Ribbons 104 G. Representation by a Concatenation of Primitive Forms 105 H. Relational Graphs 105 vi I. Recognition . 105 E, Recovery of Intrinsic Image Characteristics .... 106 A. Basic Approach 106 B. Shape from Shading 109 C. Stereoscopic Approach 110 D. Photometric Stereo 110 E. Shape from Texture Ill F. Shape from Contour Ill G. Shape and Velocity from Motion Ill F. Higher Levels of Representation 113 A. Volumetric Models 113 1. Generalized Cones . 113 2. Wire Frame Models 113 3. Polyhedral Models 113 4. Combining ID, 2D, and 3D Primitives .... 114 5. Planes and Ellipsoids 114 6. Sets of Prototype Volumes 114 B. Symbolic Descriptions 114 C. Procedural Models 115 G. Higher Levels of Interpretation 116 H, . Tracking 118 JE. Additional Tables of Model-Based Vision 122 £. Tables of Commerically Available Systems 135 K. Glossary 146 L. Publication Sources for Further Information 154 vii Figures 1. A Framework for Early and Intermediate States in a Theory of Visual Information Processing 12 2. An Example of a 2-1/2D Sketch 13 3. Examples of Applications of Computer Vision Now Underway 17 4. Model-Based Interpretation of Images 20 5. Basic Image Understanding Paradigms 23 6. Computational Architecture for a General-Purpose Vision System 30 7. Organization of a Visual System 32 8. Intensity Variations at Step Edges 89 9. A Set of Intrinsic Images Derived from a Single Monochrome Intensity Image 108 viii Tables I. Examples of Non-Linear Filtering for Extracting Edge and Line Elements 91 1 1 — 1 . Model-Based Vision Systems -- ACRONYM 36 II-2. Model-Based Vision Systems — VISIONS 37 III. [Additional] Model-Based Vision Systems 123-134 IV. Example Efforts in Industrial Visual Inspection Systems 41 V. Example Efforts in Industrial Visual Recognition and Location Systems 43-44 VI. Commercially Available Industrial Vision Systems . 136-145 VII. University Organizations Engaged in Computer Vision Research 47 VIII. Commercial Vision System Developers 48 IX. Visual Tracking Approaches 119-120 X. Example Future Applications for Computer Vision Systems 76 . Computer Vision I . Introduction Following the lead of Cohen and Feigenbaum (1982, p. 127) we may consider computer vision to be the information-processing task of understanding a scene from its projected images. Other fields such as image processing and pattern recognition also utilize computers in vision tasks. However, we can distinguish the fields by categorizing them as follows: Image processing is a signal processing task that transforms an input image into a more desirable output image through processes such as noise reduction, contrast enhancement and registr ation Pattern recognition is a classification task that classifies images into predetermined categories. \. £o rn£u t.e r_ vision is an image understanding task that automatically builds a description not only of the image itself, but of the three dimensional scene that it depicts. The term scene analysis has been used in the past to emphasize the distinction between processing two dimensional images, as in pattern c 1 a s s i f i c i a t ion and seeking information about three- , dimensional scenes.
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